Overview

Dataset statistics

Number of variables35
Number of observations9667
Missing cells1335
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 MiB
Average record size in memory266.0 B

Variable types

Numeric14
Categorical14
Boolean7

Alerts

diag_1 has a high cardinality: 447 distinct values High cardinality
diag_2 has a high cardinality: 440 distinct values High cardinality
diag_3 has a high cardinality: 468 distinct values High cardinality
number_inpatient is highly correlated with predicted_readmittedHigh correlation
predicted_readmitted is highly correlated with number_inpatientHigh correlation
change is highly correlated with diabetesMedHigh correlation
diabetesMed is highly correlated with change and 1 other fieldsHigh correlation
insulin is highly correlated with diabetesMedHigh correlation
gender is highly correlated with hemoglobin_levelHigh correlation
age is highly correlated with medical_specialtyHigh correlation
admission_type_code is highly correlated with admission_source_code and 1 other fieldsHigh correlation
admission_source_code is highly correlated with admission_type_code and 1 other fieldsHigh correlation
medical_specialty is highly correlated with age and 2 other fieldsHigh correlation
number_inpatient is highly correlated with predicted_readmittedHigh correlation
hemoglobin_level is highly correlated with genderHigh correlation
insulin is highly correlated with change and 1 other fieldsHigh correlation
change is highly correlated with insulin and 1 other fieldsHigh correlation
diabetesMed is highly correlated with insulin and 1 other fieldsHigh correlation
predicted_readmitted is highly correlated with number_inpatientHigh correlation
age has 293 (3.0%) missing values Missing
weight has 165 (1.7%) missing values Missing
admission_type_code has 146 (1.5%) missing values Missing
num_lab_procedures has 183 (1.9%) missing values Missing
num_medications has 308 (3.2%) missing values Missing
diag_2 has 169 (1.7%) missing values Missing
number_emergency is highly skewed (γ1 = 32.76633298) Skewed
admission_id has unique values Unique
num_procedures has 4369 (45.2%) zeros Zeros
number_outpatient has 8069 (83.5%) zeros Zeros
number_emergency has 8587 (88.8%) zeros Zeros
number_inpatient has 6470 (66.9%) zeros Zeros

Reproduction

Analysis started2022-02-26 12:23:27.895765
Analysis finished2022-02-26 12:24:23.512870
Duration55.62 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

admission_id
Real number (ℝ≥0)

UNIQUE

Distinct9667
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91391.86956
Minimum81412
Maximum101440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.6 KiB
2022-02-26T12:24:23.735736image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum81412
5-th percentile82461.3
Q186436
median91351
Q396379.5
95-th percentile100379.1
Maximum101440
Range20028
Interquartile range (IQR)9943.5

Descriptive statistics

Standard deviation5744.869987
Coefficient of variation (CV)0.06285974907
Kurtosis-1.194772641
Mean91391.86956
Median Absolute Deviation (MAD)4976
Skewness0.005727603675
Sum883485203
Variance33003531.17
MonotonicityNot monotonic
2022-02-26T12:24:23.985144image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
983041
 
< 0.1%
846121
 
< 0.1%
887101
 
< 0.1%
980701
 
< 0.1%
928081
 
< 0.1%
846201
 
< 0.1%
989571
 
< 0.1%
887181
 
< 0.1%
866711
 
< 0.1%
907691
 
< 0.1%
Other values (9657)9657
99.9%
ValueCountFrequency (%)
814121
< 0.1%
814151
< 0.1%
814171
< 0.1%
814181
< 0.1%
814191
< 0.1%
814201
< 0.1%
814261
< 0.1%
814271
< 0.1%
814291
< 0.1%
814311
< 0.1%
ValueCountFrequency (%)
1014401
< 0.1%
1014381
< 0.1%
1014371
< 0.1%
1014351
< 0.1%
1014321
< 0.1%
1014301
< 0.1%
1014291
< 0.1%
1014221
< 0.1%
1014161
< 0.1%
1014151
< 0.1%

patient_id
Real number (ℝ≥0)

Distinct9192
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108414208.8
Minimum10368
Maximum378731656
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.6 KiB
2022-02-26T12:24:24.182300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum10368
5-th percentile2789460
Q146745865
median91105920
Q3175244589
95-th percentile222671138.4
Maximum378731656
Range378721288
Interquartile range (IQR)128498724

Descriptive statistics

Standard deviation77557059.95
Coefficient of variation (CV)0.7153772632
Kurtosis-0.302769872
Mean108414208.8
Median Absolute Deviation (MAD)66425958
Skewness0.4830140605
Sum1.048040156 × 1012
Variance6.015097548 × 1015
MonotonicityNot monotonic
2022-02-26T12:24:24.374310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
862817406
 
0.1%
7698065
 
0.1%
30613685
 
0.1%
741936604
 
< 0.1%
467969044
 
< 0.1%
1775793424
 
< 0.1%
850268524
 
< 0.1%
1864113123
 
< 0.1%
503039163
 
< 0.1%
2006458203
 
< 0.1%
Other values (9182)9626
99.6%
ValueCountFrequency (%)
103681
< 0.1%
133201
< 0.1%
133741
< 0.1%
163441
< 0.1%
229501
< 0.1%
248221
< 0.1%
260101
< 0.1%
295201
< 0.1%
374581
< 0.1%
442441
< 0.1%
ValueCountFrequency (%)
3787316561
< 0.1%
3785156201
< 0.1%
3783907721
< 0.1%
3783389501
< 0.1%
3774073241
< 0.1%
3765696941
< 0.1%
3762382781
< 0.1%
3758470481
< 0.1%
3735359381
< 0.1%
3735146081
< 0.1%

race
Categorical

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size75.6 KiB
Caucasian
5824 
AfricanAmerican
1091 
White
699 
WHITE
 
367
African American
 
351
Other values (10)
1335 

Length

Max length16
Median length9
Mean length9.135512569
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfricanAmerican
2nd rowCaucasian
3rd rowAfrican American
4th rowAfricanAmerican
5th rowHispanic

Common Values

ValueCountFrequency (%)
Caucasian5824
60.2%
AfricanAmerican1091
 
11.3%
White699
 
7.2%
WHITE367
 
3.8%
African American351
 
3.6%
European264
 
2.7%
?226
 
2.3%
Afro American174
 
1.8%
Other166
 
1.7%
Hispanic158
 
1.6%
Other values (5)347
 
3.6%

Length

2022-02-26T12:24:24.570937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
caucasian5824
57.1%
africanamerican1110
 
10.9%
white1066
 
10.5%
american525
 
5.2%
african351
 
3.4%
european264
 
2.6%
226
 
2.2%
afro174
 
1.7%
other166
 
1.6%
hispanic158
 
1.6%
Other values (4)328
 
3.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

gender
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.6 KiB
Female
5224 
Male
4442 
Unknown/Invalid
 
1

Length

Max length15
Median length6
Mean length5.081928209
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Female5224
54.0%
Male4442
46.0%
Unknown/Invalid1
 
< 0.1%

Length

2022-02-26T12:24:24.741093image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-26T12:24:24.857052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
female5224
54.0%
male4442
46.0%
unknown/invalid1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

age
Categorical

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)0.1%
Missing293
Missing (%)3.0%
Memory size75.6 KiB
[70-80)
2465 
[60-70)
2109 
[50-60)
1601 
[80-90)
1493 
[40-50)
911 
Other values (5)
795 

Length

Max length8
Median length7
Mean length7.024855985
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[50-60)
2nd row[60-70)
3rd row[60-70)
4th row[30-40)
5th row[50-60)

Common Values

ValueCountFrequency (%)
[70-80)2465
25.5%
[60-70)2109
21.8%
[50-60)1601
16.6%
[80-90)1493
15.4%
[40-50)911
 
9.4%
[30-40)334
 
3.5%
[90-100)243
 
2.5%
[20-30)150
 
1.6%
[10-20)58
 
0.6%
[0-10)10
 
0.1%
(Missing)293
 
3.0%

Length

2022-02-26T12:24:24.965223image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-26T12:24:25.127046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
70-802465
26.3%
60-702109
22.5%
50-601601
17.1%
80-901493
15.9%
40-50911
 
9.7%
30-40334
 
3.6%
90-100243
 
2.6%
20-30150
 
1.6%
10-2058
 
0.6%
0-1010
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

weight
Categorical

MISSING

Distinct9
Distinct (%)0.1%
Missing165
Missing (%)1.7%
Memory size75.6 KiB
?
9198 
[75-100)
 
129
[50-75)
 
75
[100-125)
 
68
[125-150)
 
15
Other values (4)
 
17

Length

Max length9
Median length1
Mean length1.223847611
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row?
2nd row?
3rd row?
4th row?
5th row?

Common Values

ValueCountFrequency (%)
?9198
95.1%
[75-100)129
 
1.3%
[50-75)75
 
0.8%
[100-125)68
 
0.7%
[125-150)15
 
0.2%
[25-50)10
 
0.1%
[150-175)4
 
< 0.1%
[0-25)2
 
< 0.1%
[175-200)1
 
< 0.1%
(Missing)165
 
1.7%

Length

2022-02-26T12:24:25.287548image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-26T12:24:25.448353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
9198
96.8%
75-100129
 
1.4%
50-7575
 
0.8%
100-12568
 
0.7%
125-15015
 
0.2%
25-5010
 
0.1%
150-1754
 
< 0.1%
0-252
 
< 0.1%
175-2001
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

admission_type_code
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct7
Distinct (%)0.1%
Missing146
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean2.035080349
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.6 KiB
2022-02-26T12:24:25.576729image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.445737111
Coefficient of variation (CV)0.7104078775
Kurtosis1.95929003
Mean2.035080349
Median Absolute Deviation (MAD)0
Skewness1.585357004
Sum19376
Variance2.090155794
MonotonicityNot monotonic
2022-02-26T12:24:25.747622image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
14986
51.6%
31807
 
18.7%
21755
 
18.2%
6491
 
5.1%
5447
 
4.6%
833
 
0.3%
72
 
< 0.1%
(Missing)146
 
1.5%
ValueCountFrequency (%)
14986
51.6%
21755
 
18.2%
31807
 
18.7%
5447
 
4.6%
6491
 
5.1%
72
 
< 0.1%
833
 
0.3%
ValueCountFrequency (%)
833
 
0.3%
72
 
< 0.1%
6491
 
5.1%
5447
 
4.6%
31807
 
18.7%
21755
 
18.2%
14986
51.6%

discharge_disposition_code
Real number (ℝ≥0)

Distinct21
Distinct (%)0.2%
Missing71
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean3.614214256
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.6 KiB
2022-02-26T12:24:25.907528image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile18
Maximum28
Range27
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.261180766
Coefficient of variation (CV)1.455691443
Kurtosis6.453635621
Mean3.614214256
Median Absolute Deviation (MAD)0
Skewness2.665817523
Sum34682
Variance27.68002305
MonotonicityNot monotonic
2022-02-26T12:24:26.065825image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
15768
59.7%
31341
 
13.9%
61238
 
12.8%
18378
 
3.9%
2201
 
2.1%
22181
 
1.9%
5118
 
1.2%
2595
 
1.0%
470
 
0.7%
755
 
0.6%
Other values (11)151
 
1.6%
(Missing)71
 
0.7%
ValueCountFrequency (%)
15768
59.7%
2201
 
2.1%
31341
 
13.9%
470
 
0.7%
5118
 
1.2%
61238
 
12.8%
755
 
0.6%
811
 
0.1%
91
 
< 0.1%
121
 
< 0.1%
ValueCountFrequency (%)
2812
 
0.1%
271
 
< 0.1%
2595
 
1.0%
2350
 
0.5%
22181
1.9%
18378
3.9%
171
 
< 0.1%
161
 
< 0.1%
154
 
< 0.1%
1433
 
0.3%

admission_source_code
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.737767663
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.6 KiB
2022-02-26T12:24:26.229005image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median7
Q37
95-th percentile17
Maximum20
Range19
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.07144758
Coefficient of variation (CV)0.7095873899
Kurtosis1.698477063
Mean5.737767663
Median Absolute Deviation (MAD)0
Skewness1.025009295
Sum55467
Variance16.57668539
MonotonicityNot monotonic
2022-02-26T12:24:26.389893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
75452
56.4%
12834
29.3%
17648
 
6.7%
4302
 
3.1%
6191
 
2.0%
2114
 
1.2%
576
 
0.8%
319
 
0.2%
2014
 
0.1%
914
 
0.1%
Other values (2)3
 
< 0.1%
ValueCountFrequency (%)
12834
29.3%
2114
 
1.2%
319
 
0.2%
4302
 
3.1%
576
 
0.8%
6191
 
2.0%
75452
56.4%
81
 
< 0.1%
914
 
0.1%
102
 
< 0.1%
ValueCountFrequency (%)
2014
 
0.1%
17648
 
6.7%
102
 
< 0.1%
914
 
0.1%
81
 
< 0.1%
75452
56.4%
6191
 
2.0%
576
 
0.8%
4302
 
3.1%
319
 
0.2%

time_in_hospital
Real number (ℝ≥0)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.391124444
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.6 KiB
2022-02-26T12:24:26.570141image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum14
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.004172532
Coefficient of variation (CV)0.6841465257
Kurtosis0.8078394717
Mean4.391124444
Median Absolute Deviation (MAD)2
Skewness1.130043444
Sum42449
Variance9.025052605
MonotonicityNot monotonic
2022-02-26T12:24:26.758270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
31687
17.5%
21650
17.1%
11382
14.3%
41273
13.2%
5945
9.8%
6718
7.4%
7541
 
5.6%
8420
 
4.3%
9292
 
3.0%
10214
 
2.2%
Other values (4)545
 
5.6%
ValueCountFrequency (%)
11382
14.3%
21650
17.1%
31687
17.5%
41273
13.2%
5945
9.8%
6718
7.4%
7541
 
5.6%
8420
 
4.3%
9292
 
3.0%
10214
 
2.2%
ValueCountFrequency (%)
1493
 
1.0%
13126
 
1.3%
12140
 
1.4%
11186
 
1.9%
10214
 
2.2%
9292
 
3.0%
8420
4.3%
7541
5.6%
6718
7.4%
5945
9.8%

payer_code
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size75.6 KiB
?
3833 
MC
3031 
HM
612 
SP
490 
BC
454 
Other values (12)
1247 

Length

Max length2
Median length2
Mean length1.603496431
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row?
2nd rowMC
3rd row?
4th rowBC
5th row?

Common Values

ValueCountFrequency (%)
?3833
39.7%
MC3031
31.4%
HM612
 
6.3%
SP490
 
5.1%
BC454
 
4.7%
MD331
 
3.4%
UN243
 
2.5%
CP212
 
2.2%
CM195
 
2.0%
OG119
 
1.2%
Other values (7)147
 
1.5%

Length

2022-02-26T12:24:26.943146image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3833
39.7%
mc3031
31.4%
hm612
 
6.3%
sp490
 
5.1%
bc454
 
4.7%
md331
 
3.4%
un243
 
2.5%
cp212
 
2.2%
cm195
 
2.0%
og119
 
1.2%
Other values (7)147
 
1.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

medical_specialty
Categorical

HIGH CORRELATION

Distinct50
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size75.6 KiB
?
4690 
InternalMedicine
1353 
Emergency/Trauma
757 
Family/GeneralPractice
736 
Cardiology
507 
Other values (45)
1624 

Length

Max length36
Median length9
Mean length8.741388228
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st rowInternalMedicine
2nd rowRadiologist
3rd row?
4th rowFamily/GeneralPractice
5th row?

Common Values

ValueCountFrequency (%)
?4690
48.5%
InternalMedicine1353
 
14.0%
Emergency/Trauma757
 
7.8%
Family/GeneralPractice736
 
7.6%
Cardiology507
 
5.2%
Surgery-General297
 
3.1%
Orthopedics139
 
1.4%
Orthopedics-Reconstructive132
 
1.4%
Radiologist125
 
1.3%
Nephrology122
 
1.3%
Other values (40)809
 
8.4%

Length

2022-02-26T12:24:27.132619image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4690
48.5%
internalmedicine1353
 
14.0%
emergency/trauma757
 
7.8%
family/generalpractice736
 
7.6%
cardiology507
 
5.2%
surgery-general297
 
3.1%
orthopedics139
 
1.4%
orthopedics-reconstructive132
 
1.4%
radiologist125
 
1.3%
nephrology122
 
1.3%
Other values (40)809
 
8.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.6 KiB
False
9575 
True
 
92
ValueCountFrequency (%)
False9575
99.0%
True92
 
1.0%
2022-02-26T12:24:27.286016image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.6 KiB
Complete
8082 
Incomplete
1547 
None
 
38

Length

Max length10
Median length8
Mean length8.304334333
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowComplete
2nd rowComplete
3rd rowComplete
4th rowComplete
5th rowComplete

Common Values

ValueCountFrequency (%)
Complete8082
83.6%
Incomplete1547
 
16.0%
None38
 
0.4%

Length

2022-02-26T12:24:27.384720image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-26T12:24:27.489761image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
complete8082
83.6%
incomplete1547
 
16.0%
none38
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

num_lab_procedures
Real number (ℝ≥0)

MISSING

Distinct105
Distinct (%)1.1%
Missing183
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean42.949283
Minimum1
Maximum111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.6 KiB
2022-02-26T12:24:27.614096image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q131
median44
Q357
95-th percentile73
Maximum111
Range110
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.87162906
Coefficient of variation (CV)0.4626766192
Kurtosis-0.2838583369
Mean42.949283
Median Absolute Deviation (MAD)13
Skewness-0.2406325089
Sum407331
Variance394.8816414
MonotonicityNot monotonic
2022-02-26T12:24:27.843169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1320
 
3.3%
43255
 
2.6%
49219
 
2.3%
42217
 
2.2%
45217
 
2.2%
44215
 
2.2%
40205
 
2.1%
39205
 
2.1%
46203
 
2.1%
37197
 
2.0%
Other values (95)7231
74.8%
ValueCountFrequency (%)
1320
3.3%
2109
 
1.1%
355
 
0.6%
437
 
0.4%
531
 
0.3%
634
 
0.4%
724
 
0.2%
843
 
0.4%
985
 
0.9%
1092
 
1.0%
ValueCountFrequency (%)
1111
 
< 0.1%
1091
 
< 0.1%
1071
 
< 0.1%
1031
 
< 0.1%
1021
 
< 0.1%
1012
< 0.1%
1003
< 0.1%
981
 
< 0.1%
974
< 0.1%
961
 
< 0.1%

num_procedures
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.350884452
Minimum0
Maximum6
Zeros4369
Zeros (%)45.2%
Negative0
Negative (%)0.0%
Memory size75.6 KiB
2022-02-26T12:24:28.038185image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.704893698
Coefficient of variation (CV)1.262057384
Kurtosis0.8315415433
Mean1.350884452
Median Absolute Deviation (MAD)1
Skewness1.306173765
Sum13059
Variance2.906662522
MonotonicityNot monotonic
2022-02-26T12:24:28.198481image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
04369
45.2%
12004
20.7%
21229
 
12.7%
3911
 
9.4%
6461
 
4.8%
4367
 
3.8%
5326
 
3.4%
ValueCountFrequency (%)
04369
45.2%
12004
20.7%
21229
 
12.7%
3911
 
9.4%
4367
 
3.8%
5326
 
3.4%
6461
 
4.8%
ValueCountFrequency (%)
6461
 
4.8%
5326
 
3.4%
4367
 
3.8%
3911
 
9.4%
21229
 
12.7%
12004
20.7%
04369
45.2%

num_medications
Real number (ℝ≥0)

MISSING

Distinct64
Distinct (%)0.7%
Missing308
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean16.07041351
Minimum1
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.6 KiB
2022-02-26T12:24:28.411827image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median15
Q320
95-th percentile31
Maximum79
Range78
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.281684625
Coefficient of variation (CV)0.5153373696
Kurtosis3.559094607
Mean16.07041351
Median Absolute Deviation (MAD)5
Skewness1.382556006
Sum150403
Variance68.58630022
MonotonicityNot monotonic
2022-02-26T12:24:28.647771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15565
 
5.8%
13563
 
5.8%
12555
 
5.7%
11549
 
5.7%
10510
 
5.3%
14502
 
5.2%
16482
 
5.0%
17445
 
4.6%
9442
 
4.6%
18405
 
4.2%
Other values (54)4341
44.9%
ValueCountFrequency (%)
126
 
0.3%
251
 
0.5%
383
 
0.9%
4109
 
1.1%
5173
 
1.8%
6242
2.5%
7352
3.6%
8404
4.2%
9442
4.6%
10510
5.3%
ValueCountFrequency (%)
791
 
< 0.1%
661
 
< 0.1%
651
 
< 0.1%
614
< 0.1%
603
< 0.1%
593
< 0.1%
586
0.1%
573
< 0.1%
564
< 0.1%
553
< 0.1%

number_outpatient
Real number (ℝ≥0)

ZEROS

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3639184856
Minimum0
Maximum21
Zeros8069
Zeros (%)83.5%
Negative0
Negative (%)0.0%
Memory size75.6 KiB
2022-02-26T12:24:28.859503image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.188595663
Coefficient of variation (CV)3.266104113
Kurtosis70.08823199
Mean0.3639184856
Median Absolute Deviation (MAD)0
Skewness6.71289012
Sum3518
Variance1.412759649
MonotonicityNot monotonic
2022-02-26T12:24:29.023452image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
08069
83.5%
1822
 
8.5%
2330
 
3.4%
3211
 
2.2%
4106
 
1.1%
549
 
0.5%
630
 
0.3%
811
 
0.1%
78
 
0.1%
95
 
0.1%
Other values (9)26
 
0.3%
ValueCountFrequency (%)
08069
83.5%
1822
 
8.5%
2330
 
3.4%
3211
 
2.2%
4106
 
1.1%
549
 
0.5%
630
 
0.3%
78
 
0.1%
811
 
0.1%
95
 
0.1%
ValueCountFrequency (%)
212
 
< 0.1%
191
 
< 0.1%
173
< 0.1%
161
 
< 0.1%
154
< 0.1%
144
< 0.1%
132
 
< 0.1%
115
0.1%
104
< 0.1%
95
0.1%

number_emergency
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1894072618
Minimum0
Maximum63
Zeros8587
Zeros (%)88.8%
Negative0
Negative (%)0.0%
Memory size75.6 KiB
2022-02-26T12:24:29.204602image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum63
Range63
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9521011049
Coefficient of variation (CV)5.026740241
Kurtosis1983.856156
Mean0.1894072618
Median Absolute Deviation (MAD)0
Skewness32.76633298
Sum1831
Variance0.9064965139
MonotonicityNot monotonic
2022-02-26T12:24:29.375399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
08587
88.8%
1747
 
7.7%
2195
 
2.0%
357
 
0.6%
442
 
0.4%
78
 
0.1%
57
 
0.1%
66
 
0.1%
86
 
0.1%
104
 
< 0.1%
Other values (5)8
 
0.1%
ValueCountFrequency (%)
08587
88.8%
1747
 
7.7%
2195
 
2.0%
357
 
0.6%
442
 
0.4%
57
 
0.1%
66
 
0.1%
78
 
0.1%
86
 
0.1%
92
 
< 0.1%
ValueCountFrequency (%)
631
 
< 0.1%
131
 
< 0.1%
122
 
< 0.1%
112
 
< 0.1%
104
< 0.1%
92
 
< 0.1%
86
0.1%
78
0.1%
66
0.1%
57
0.1%

number_inpatient
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6270818248
Minimum0
Maximum16
Zeros6470
Zeros (%)66.9%
Negative0
Negative (%)0.0%
Memory size75.6 KiB
2022-02-26T12:24:29.554178image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum16
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.255677361
Coefficient of variation (CV)2.002413897
Kurtosis21.17006569
Mean0.6270818248
Median Absolute Deviation (MAD)0
Skewness3.644988807
Sum6062
Variance1.576725634
MonotonicityNot monotonic
2022-02-26T12:24:29.744625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
06470
66.9%
11831
 
18.9%
2694
 
7.2%
3325
 
3.4%
4156
 
1.6%
585
 
0.9%
647
 
0.5%
817
 
0.2%
716
 
0.2%
108
 
0.1%
Other values (7)18
 
0.2%
ValueCountFrequency (%)
06470
66.9%
11831
 
18.9%
2694
 
7.2%
3325
 
3.4%
4156
 
1.6%
585
 
0.9%
647
 
0.5%
716
 
0.2%
817
 
0.2%
96
 
0.1%
ValueCountFrequency (%)
161
 
< 0.1%
152
 
< 0.1%
142
 
< 0.1%
131
 
< 0.1%
122
 
< 0.1%
114
 
< 0.1%
108
0.1%
96
 
0.1%
817
0.2%
716
0.2%

diag_1
Categorical

HIGH CARDINALITY

Distinct447
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size75.6 KiB
428
 
684
414
 
666
786
 
372
410
 
321
486
 
316
Other values (442)
7308 

Length

Max length6
Median length3
Mean length3.16695976
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique110 ?
Unique (%)1.1%

Sample

1st row183
2nd row414
3rd row428
4th row562
5th row808

Common Values

ValueCountFrequency (%)
428684
 
7.1%
414666
 
6.9%
786372
 
3.8%
410321
 
3.3%
486316
 
3.3%
427272
 
2.8%
491231
 
2.4%
715227
 
2.3%
682212
 
2.2%
434203
 
2.1%
Other values (437)6163
63.8%

Length

2022-02-26T12:24:29.924082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
428684
 
7.1%
414666
 
6.9%
786372
 
3.8%
410321
 
3.3%
486316
 
3.3%
427272
 
2.8%
491231
 
2.4%
715227
 
2.3%
682212
 
2.2%
434203
 
2.1%
Other values (437)6163
63.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diag_2
Categorical

HIGH CARDINALITY
MISSING

Distinct440
Distinct (%)4.6%
Missing169
Missing (%)1.7%
Memory size75.6 KiB
428
 
653
276
 
613
250
 
598
427
 
462
401
 
368
Other values (435)
6804 

Length

Max length6
Median length3
Mean length3.16614024
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique128 ?
Unique (%)1.3%

Sample

1st row197
2nd row411
3rd row585
4th row596
5th row861

Common Values

ValueCountFrequency (%)
428653
 
6.8%
276613
 
6.3%
250598
 
6.2%
427462
 
4.8%
401368
 
3.8%
599299
 
3.1%
496291
 
3.0%
414283
 
2.9%
411257
 
2.7%
403241
 
2.5%
Other values (430)5433
56.2%

Length

2022-02-26T12:24:30.105513image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
428653
 
6.9%
276613
 
6.5%
250598
 
6.3%
427462
 
4.9%
401368
 
3.9%
599299
 
3.1%
496291
 
3.1%
414283
 
3.0%
411257
 
2.7%
403241
 
2.5%
Other values (430)5433
57.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diag_3
Categorical

HIGH CARDINALITY

Distinct468
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size75.6 KiB
250
1165 
401
833 
276
 
510
428
 
396
427
 
383
Other values (463)
6380 

Length

Max length6
Median length3
Mean length3.105306714
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique133 ?
Unique (%)1.4%

Sample

1st row250.02
2nd row295
3rd row496
4th row585
5th row865

Common Values

ValueCountFrequency (%)
2501165
 
12.1%
401833
 
8.6%
276510
 
5.3%
428396
 
4.1%
427383
 
4.0%
414374
 
3.9%
496231
 
2.4%
403198
 
2.0%
272191
 
2.0%
599188
 
1.9%
Other values (458)5198
53.8%

Length

2022-02-26T12:24:30.286685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2501165
 
12.1%
401833
 
8.6%
276510
 
5.3%
428396
 
4.1%
427383
 
4.0%
414374
 
3.9%
496231
 
2.4%
403198
 
2.0%
272191
 
2.0%
599188
 
1.9%
Other values (458)5198
53.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

number_diagnoses
Real number (ℝ≥0)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.414502948
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.6 KiB
2022-02-26T12:24:30.460243image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q39
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.953845855
Coefficient of variation (CV)0.2635167682
Kurtosis-0.1204505695
Mean7.414502948
Median Absolute Deviation (MAD)1
Skewness-0.9130002874
Sum71676
Variance3.817513624
MonotonicityNot monotonic
2022-02-26T12:24:30.605807image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
94738
49.0%
51034
 
10.7%
81010
 
10.4%
6979
 
10.1%
7932
 
9.6%
4539
 
5.6%
3298
 
3.1%
2108
 
1.1%
122
 
0.2%
152
 
< 0.1%
Other values (4)5
 
0.1%
ValueCountFrequency (%)
122
 
0.2%
2108
 
1.1%
3298
 
3.1%
4539
 
5.6%
51034
 
10.7%
6979
 
10.1%
7932
 
9.6%
81010
 
10.4%
94738
49.0%
101
 
< 0.1%
ValueCountFrequency (%)
162
 
< 0.1%
152
 
< 0.1%
141
 
< 0.1%
121
 
< 0.1%
101
 
< 0.1%
94738
49.0%
81010
 
10.4%
7932
 
9.6%
6979
 
10.1%
51034
 
10.7%

blood_type
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size75.6 KiB
O+
3788 
A+
2963 
B+
1071 
O-
699 
A-
574 
Other values (3)
572 

Length

Max length3
Median length2
Mean length2.04261922
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowO+
2nd rowO-
3rd rowO-
4th rowA+
5th rowO+

Common Values

ValueCountFrequency (%)
O+3788
39.2%
A+2963
30.7%
B+1071
 
11.1%
O-699
 
7.2%
A-574
 
5.9%
AB+320
 
3.3%
B-160
 
1.7%
AB-92
 
1.0%

Length

2022-02-26T12:24:30.794114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-26T12:24:30.940098image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
o4487
46.4%
a3537
36.6%
b1231
 
12.7%
ab412
 
4.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

hemoglobin_level
Real number (ℝ≥0)

HIGH CORRELATION

Distinct70
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.18698666
Minimum10.9
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.6 KiB
2022-02-26T12:24:31.507329image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum10.9
5-th percentile12.6
Q113.4
median14.1
Q314.9
95-th percentile16
Maximum18
Range7.1
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.051107764
Coefficient of variation (CV)0.07408957163
Kurtosis-0.4462170239
Mean14.18698666
Median Absolute Deviation (MAD)0.8
Skewness0.188906029
Sum137145.6
Variance1.104827532
MonotonicityNot monotonic
2022-02-26T12:24:31.742131image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.6354
 
3.7%
13.8346
 
3.6%
13.5337
 
3.5%
14.5325
 
3.4%
13.7323
 
3.3%
13.4317
 
3.3%
13.9315
 
3.3%
14315
 
3.3%
14.4313
 
3.2%
14.3312
 
3.2%
Other values (60)6410
66.3%
ValueCountFrequency (%)
10.91
 
< 0.1%
11.11
 
< 0.1%
11.22
 
< 0.1%
11.33
 
< 0.1%
11.42
 
< 0.1%
11.52
 
< 0.1%
11.67
 
0.1%
11.711
0.1%
11.813
0.1%
11.922
0.2%
ValueCountFrequency (%)
181
 
< 0.1%
17.81
 
< 0.1%
17.72
 
< 0.1%
17.61
 
< 0.1%
17.52
 
< 0.1%
17.43
 
< 0.1%
17.31
 
< 0.1%
17.21
 
< 0.1%
17.18
0.1%
178
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.6 KiB
False
8522 
True
1145 
ValueCountFrequency (%)
False8522
88.2%
True1145
 
11.8%
2022-02-26T12:24:31.915864image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

max_glu_serum
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size75.6 KiB
None
6426 
NONE
2735 
Norm
 
165
>200
 
147
>300
 
101

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNONE
5th rowNone

Common Values

ValueCountFrequency (%)
None6426
66.5%
NONE2735
28.3%
Norm165
 
1.7%
>200147
 
1.5%
>300101
 
1.0%
NORM93
 
1.0%

Length

2022-02-26T12:24:32.006402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-26T12:24:32.147978image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
none9161
94.8%
norm258
 
2.7%
200147
 
1.5%
300101
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

A1Cresult
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.6 KiB
None
8045 
>8
 
797
Norm
 
477
>7
 
348

Length

Max length4
Median length4
Mean length3.763111617
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None8045
83.2%
>8797
 
8.2%
Norm477
 
4.9%
>7348
 
3.6%

Length

2022-02-26T12:24:32.281403image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-26T12:24:32.428728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
none8045
83.2%
8797
 
8.2%
norm477
 
4.9%
7348
 
3.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diuretics
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
False
9489 
True
 
178
ValueCountFrequency (%)
False9489
98.2%
True178
 
1.8%
2022-02-26T12:24:32.492637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

insulin
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
True
5246 
False
4421 
ValueCountFrequency (%)
True5246
54.3%
False4421
45.7%
2022-02-26T12:24:32.542496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

change
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.6 KiB
No
5151 
Ch
4516 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCh
2nd rowCh
3rd rowNo
4th rowNo
5th rowCh

Common Values

ValueCountFrequency (%)
No5151
53.3%
Ch4516
46.7%

Length

2022-02-26T12:24:32.628891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-26T12:24:32.743035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
no5151
53.3%
ch4516
46.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diabetesMed
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
True
7471 
False
2196 
ValueCountFrequency (%)
True7471
77.3%
False2196
 
22.7%
2022-02-26T12:24:32.794310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

predicted_readmitted
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
False
6174 
True
3493 
ValueCountFrequency (%)
False6174
63.9%
True3493
36.1%
2022-02-26T12:24:32.843596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
False
8570 
True
1097 
ValueCountFrequency (%)
False8570
88.7%
True1097
 
11.3%
2022-02-26T12:24:32.891753image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Interactions

2022-02-26T12:24:17.230526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:38.473508image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:41.378399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:44.278419image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:47.103719image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:50.021133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:52.857085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:55.666093image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:58.776157image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:01.545557image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:04.455328image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:07.434906image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:10.627978image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:14.169528image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:17.438905image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:38.806022image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:41.578828image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:44.468807image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:47.309468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:50.233903image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:53.049313image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:55.874118image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:58.981938image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:01.758899image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:04.657895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:07.633731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:10.882659image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:14.444494image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:17.644248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:38.984718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:41.778509image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:44.664893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:47.502356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:50.435404image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:53.251638image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:56.077247image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:59.174094image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:01.957561image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:04.853475image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:07.843540image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:11.125242image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:14.713801image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:17.841815image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:39.180773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:41.973799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:44.857153image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:47.715738image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:50.630513image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:53.453749image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:56.282567image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:59.385866image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:02.166625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:05.028116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:08.042364image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:11.358374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:14.929936image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:18.042571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:39.376616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:42.167037image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:45.045223image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:47.906029image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:50.841897image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:53.641465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:56.485609image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:59.579504image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:02.380569image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:05.213799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:08.264049image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:11.578516image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:15.137564image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:18.247449image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:39.583616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:42.379230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:45.259577image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:48.116592image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:51.056949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:53.837777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:56.710747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:59.794065image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:02.600048image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:05.415178image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:08.503570image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:11.820813image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:15.341995image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:18.450493image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:39.776089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:42.578427image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:45.447224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:48.315959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:51.268706image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:54.024013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:56.925714image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:59.983175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:02.813762image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:05.612310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:08.719238image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:12.045508image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:15.543874image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:18.640495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:39.964907image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:42.773446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:45.630637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:48.489554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:51.458759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:54.233124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:57.424351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:00.175374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:03.001948image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:05.803877image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:08.932772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:12.278516image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:15.735960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:19.199334image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:40.145187image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:42.974005image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:45.821252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:48.697227image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:51.668878image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:54.429647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:57.638294image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:00.371534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:03.212125image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:05.991624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:09.163203image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:12.535334image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:15.938776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:19.387551image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:40.348855image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:43.172255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:46.028432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:48.893223image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:51.881469image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:54.642436image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:57.828538image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:00.561687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:03.412453image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:06.184207image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:09.437133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:12.831732image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:16.157578image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:19.588048image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:40.545675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:43.356933image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:46.220092image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:49.080168image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:52.058962image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:54.853758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:58.005584image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:00.748232image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:03.602745image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:06.672437image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:09.720174image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:13.071330image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:16.340797image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:19.790554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:40.742424image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:43.700712image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:46.413097image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:49.270603image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:52.252046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:55.052988image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:58.200912image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:00.946201image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:03.823821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:06.858718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:09.947722image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:13.320141image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:16.546637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:19.984055image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:40.954671image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:43.878572image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:46.607256image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:49.461149image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:52.450851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:55.268044image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:58.390765image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:01.145255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:04.020946image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:07.062236image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:10.180833image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:13.618710image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:16.828850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:20.185933image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:41.166680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:44.082130image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:46.874475image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:49.849429image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:52.657631image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:55.467165image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:23:58.582192image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:01.341887image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:04.232695image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:07.245257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:10.411136image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:13.891870image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-26T12:24:17.025358image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-02-26T12:24:33.022778image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-26T12:24:33.311157image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-26T12:24:33.597449image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-26T12:24:33.896813image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-26T12:24:34.225411image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-26T12:24:20.686959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-26T12:24:22.227590image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-26T12:24:22.840690image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-26T12:24:23.130286image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

admission_idpatient_idracegenderageweightadmission_type_codedischarge_disposition_codeadmission_source_codetime_in_hospitalpayer_codemedical_specialtyhas_prosthesiscomplete_vaccination_statusnum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientdiag_1diag_2diag_3number_diagnosesblood_typehemoglobin_levelblood_transfusionmax_glu_serumA1CresultdiureticsinsulinchangediabetesMedpredicted_readmittedactual_readmitted
097265215370.0AfricanAmericanFemaleNaN?1.01.07.012?InternalMedicineFalseComplete72.05.030.00.00.00.0183197250.029.0O+12.0FalseNoneNoneNoYesChYesFalseFalse
189653168821964.0CaucasianMale[50-60)?3.01.01.01MCRadiologistFalseComplete34.06.024.00.00.01.04144112959.0O-15.3FalseNoneNoneYesYesChYesFalseFalse
2995181020438.0African AmericanMale[60-70)?2.018.02.02??FalseComplete32.01.015.04.00.04.04285854969.0O-14.1FalseNoneNoneNoYesNoYesTrueFalse
389397141934014.0AfricanAmericanFemale[60-70)?3.01.01.05BCFamily/GeneralPracticeFalseComplete57.02.030.00.00.00.05625965859.0A+13.4FalseNONENoneNoYesNoYesFalseFalse
4832786485868.0HispanicMale[30-40)?1.03.07.014??FalseComplete89.06.048.00.00.00.08088618659.0O+14.6TrueNoneNoneNoYesChYesTrueFalse
591411227900106.0CaucasianMale[50-60)?3.01.01.01BC?FalseIncomplete37.01.017.02.00.00.0175V867806.0A+13.7FalseNoneNoneNoYesChYesFalseFalse
6100827122924754.0CaucasianFemale[80-90)?1.03.07.02MC?FalseComplete3.00.016.00.00.00.038599250.039.0O-14.0FalseNoneNoneNoYesChYesTrueFalse
79631070917840.0OtherMale[70-80)?2.01.07.03OGFamily/GeneralPracticeFalseIncomplete1.00.0NaN0.00.00.05992504014.0O+15.1FalseNONENoneNoYesChYesFalseFalse
889425169838640.0CaucasianFemale[40-50)?2.01.01.06HM?FalseComplete54.01.024.00.00.00.0250.87076829.0B+13.4FalseNoneNormNoNoNoYesFalseFalse
9101072114696756.0CaucasianMale[60-70)?1.01.07.02MC?FalseComplete44.00.017.00.00.00.0852NaN4149.0A+14.8FalseNoneNoneNoYesChYesFalseTrue

Last rows

admission_idpatient_idracegenderageweightadmission_type_codedischarge_disposition_codeadmission_source_codetime_in_hospitalpayer_codemedical_specialtyhas_prosthesiscomplete_vaccination_statusnum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientdiag_1diag_2diag_3number_diagnosesblood_typehemoglobin_levelblood_transfusionmax_glu_serumA1CresultdiureticsinsulinchangediabetesMedpredicted_readmittedactual_readmitted
965785120186411312.0CaucasianMale[40-50)?2.01.07.03OGEmergency/TraumaTrueComplete21.03.021.05.07.02.07865772509.0O+15.3FalseNONENoneNoYesNoYesTrueFalse
96589154012305736.0CaucasianMale[70-80)?3.018.01.06??FalseComplete26.02.014.00.00.01.0V554965536.0O+13.3FalseNONENoneNoNoNoNoFalseFalse
96599561332364738.0CaucasianFemale[50-60)?2.01.01.010MCNephrologyFalseComplete40.02.024.00.00.00.0428V454035.0A+13.6TrueNoneNoneNoYesChYesFalseFalse
966010007256769552.0CaucasianMale[40-50)?1.01.017.03??FalseComplete22.01.017.00.00.00.05787807079.0O+13.1FalseNormNoneNoNoNoNoFalseFalse
9661871872176236.0CaucasianFemale[70-80)?1.01.07.04?Emergency/TraumaFalseComplete45.00.011.00.00.00.07808144589.0B+12.3FalseNone>7NoYesChYesFalseFalse
96629077244380656.0CaucasianFemale[50-60)?3.01.020.04??FalseComplete19.03.013.01.00.01.01531961986.0B+14.0FalseNoneNoneNoYesNoYesTrueFalse
966310069010796580.0WhiteFemale[60-70)?1.01.04.03?OrthopedicsFalseComplete23.01.09.00.00.00.08242507333.0O+12.7FalseNONENoneNoNoNoYesFalseFalse
96649926063624078.0OtherMale[50-60)?2.01.07.05BCEmergency/TraumaFalseComplete65.01.020.00.00.00.0574250?2.0O+13.4FalseNone>7NoYesChYesFalseFalse
966588485271206676.0CaucasianMale[20-30)?1.0NaN7.02SP?FalseComplete51.00.010.00.00.00.0250.132762763.0O+14.7FalseNone>8NoYesChYesFalseFalse
966697131126311814.0CaucasianMale[70-80)?1.01.07.02MC?FalseComplete49.01.020.00.00.01.0511585V429.0AB+15.3TrueNoneNoneNoYesChYesTrueFalse